Advanced Causal Inference Techniques

Últimos artículos

• Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects

  Anish Agarwal, Sukjin Han, Dwaipayan Saha, Vasilis Syrgkanis, Haeyeon Yoon · 14 de septiembre de 2026

  We propose a generalization of the synthetic control methods to the setting with dynamic treatment effects, in which each unit receives multiple treatments sequentially, according to an adaptive policy that depends on a latent, endogenously time-varying confounding state. Under a low-rank latent fac…

• Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables

  Yasin Ibrahim, Hermione Warr, Robin J. Evans, Konstantinos Kamnitsas · 11 de septiembre de 2026

  Machine learning models can achieve strong test performance while relying on demographic or acquisition-related shortcuts. We propose counterfactual (CF) marginalisation as a test-time evaluation procedure for assessing robustness of classification models to such variables. Given a CF image generato…

• Differentially Private Average Treatment Effect Estimation by Propensity Score Blocking

  Duncan Stewardson, Grayson W. White, Adam Groce · 10 de septiembre de 2026

  Average treatment effect (ATE) estimation in observational studies is a fundamental statistical tool used frequently in social science, medicine, and other fields. These fields often work with sensitive data where privacy protections are important, so a differentially private mechanism for ATE estim…

• Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless Networks

  Abdessamed Qchohi, Jessica Moysen Cortes, Matteo Zecchin · 7 de septiembre de 2026

  Conformal counterfactual inference enables network operators to use logged telemetry to reliably answer 'what-if' questions about network operation. These answers typically take the form of prediction sets that contain, with a user-defined probability, the key performance indicators (KPIs) that woul…

• A location-invariant estimator of extremal quantile treatment effects for heavy-tailed distributions

  Xin Yu, Shuwei Huang, Jicheng Liu, Jielin Tang, Bolin Wang, Yunxiao Zhang, Tian Zhao · 4 de septiembre de 2026

  Quantile treatment effects (QTEs) measure the effect of a treatment on the distribution of an outcome, and their estimation at extreme quantile levels is of central interest in applications where the target quantiles lie far beyond the range of the data. For heavy-tailed potential outcomes, existing…

• When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation

  Cong Cao · 2 de septiembre de 2026

  Prediction error is widely used to evaluate nuisance-function estimators in causal inference, but its relationship with causal estimator performance may differ across performance measures. We studied this question in a partially linear model using Monte Carlo simulations. We compared ordinary least …

• Difference-in-Differences on a Censored Rating Scale Can Manufacture an Effect: Evidence from a Pre-Registered LLM-Judge Audit

  Shuyi Fan, Boyuan Deng, Mengyu Xu, Xinhong Xie, Chenyang Li, Hongyang Zhang · 28 de agosto de 2026

  Audits of LLM judges certify a bias by contrasting matched conditions, and the strongest designs difference twice: a within-item contrast between two candidate responses, differenced again across a manipulated attribute, read off a bounded rating scale. We show that this endpoint is not identified o…

• DIRECT: Decomposing Audience Preference and Creative Effect in Visual Content Analytics

  Yizhi Liu, Balaji Padmanabhan, Siva Viswanathan · 28 de agosto de 2026

  Which visual choices make a post perform better? A growing literature answers this question with pooled coefficients estimated across many creators, which platforms translate into creative recommendations. We show that these coefficients blend two distinct patterns that can point in opposite directi…

• Generative AI for Validating Physics Laws

  Maria Nareklishvili, Nicholas Polson, Vadim Sokolov · 26 de agosto de 2026

  We propose generative learner for estimating heterogeneous treatment effects and characterizing the full distribution of causal effects. The learner takes the form of a multi-head feed-forward neural network with three jointly estimated subnetworks, propensity score, baseline outcome, and heterogene…

• Barycentric Fused Gromov-Wasserstein Balancing for Causal Inference under Multiple Treatments

  Yuki Murakami, Takumi Hattori, Kohsuke Kubota · 25 de agosto de 2026

  Estimating heterogeneous single and interaction treatment effects from observational data under multiple simultaneous treatments is crucial for decision-making. To mitigate estimation variance, previous studies balance representation distributions between every pair of treatment patterns. However, s…

• Causal Inference under Interference with Learned Exposure Mappings

  Cong Cao · 21 de agosto de 2026

  Exposure mappings are often assumed to be known in causal spillover analyses. In environmental settings, however, they are typically induced by transport processes that are not directly observed and must instead be learned from pollution data. We study how uncertainty in learned transport processes …

• Transportable Causal Effect Estimation across Networks under Interference

  Xiaojing Du, Jiuyong Li, Lin Liu, Debo Cheng, Jixue Liu, Thuc Duy Le · 20 de agosto de 2026

  Estimating causal effects under network interference typically assumes that the network used for training and the network used for deployment coincide. In practice, an intervention is run on one population while the question of interest concerns a different population, and the two generally differ i…

• Cross-View Correspondence Is a Measurement Intervention: Two-Sided Validation for Agent Evaluation and Credit Assignment

  Zhen Zhang, Ahmad Hafez, Amr Alanwar · 19 de agosto de 2026

  Agent evaluations and trace-based learning often compare outputs across transformed views through a post-response correspondence treated as neutral preprocessing. We show that this correspondence is a measurement intervention: omitting it can manufacture sensitivity, an over-aggressive map can manuf…

• Second-Order Policy Effects as State Transitions: A Source-Linked Benchmark for Policy Simulation

  Wesley Shu · 18 de agosto de 2026

  Policy evaluation often estimates direct benefits and costs while treating the institutional environment as fixed. In practice, a policy changes the system it enters: actors adapt, enforcement capacity shifts, burdens move, and new equilibria form around capture, gaming, compliance theater, irrevers…

• Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization

  Jiayi Dan, Bo Li, Lu Deng, Yong Wang · 14 de agosto de 2026

  Post-click conversion rate (CVR) is a key metric in various scenarios including e-commerce and advertising, reflecting the efficiency and user experience in the second stage of the conversion process. Estimating the causal effect on CVR is therefore of great practical importance. However, directly a…

• When Can You Trust Offline Evaluation of Equal-Cost Top-k Allocation? A Controlled, Reproducible Benchmark and Practitioner's Guide

  Binshuang Li · 14 de agosto de 2026

  Organizations decide whom to treat under a budget and want to know what a targeting rule would have earned before deploying it. Off-policy evaluation promises this from logged data, but the deployable rule is a deterministic top-k policy: it removes all averaging over actions, so weak overlap hits t…

• FunnelCausalNet: Funnel-aware Joint Conversion-Revenue Uplift for Multi-tier Coupon Allocation

  Yu Zhang (AMap Alibaba Group, Beijing, China), Zhihan Wang (AMap Alibaba Group, Beijing, China), Guanlin Chen (AMap Alibaba Group, Beijing, China), Min Jiang (AMap Alibaba Group, Beijing, China), Shuai Li (AMap Alibaba Group, Beijing, China) · 13 de agosto de 2026

  Coupon campaigns seek to lift both conversion and revenue, but gross merchandise value (GMV) follows a deterministic funnel from conversion to conditional order value and is zero-inflated and heavy-tailed. We propose FunnelCausalNet, an uplift estimator coupling a binary conversion head with a nonne…

• Causal inference for group-contaminated structured outcomes: observable quotients, lossless reduction and exact randomization inference

  Usef Faghihi, Amir Saki · 13 de agosto de 2026

  Structured potential outcomes such as microscopy images may be recorded after an unknown, unit-specific transformation. If that transformation can depend on treatment, covariates or the intrinsic outcome, raw-coordinate analyses may mix biological effects with acquisition geometry. We study the unre…

• From Prediction to Incrementality: Causal Optimization for Large-Scale Targeting and Recommendation

  Changshuai Wei, John Bencina, Phuc Nguyen, Andre Assuncao Silva T Ribeiro, Benjamin Zelditch · 12 de agosto de 2026

  Large-scale targeting and recommendation systems are typically built around predictive scores fed into heuristic or local allocation. When the business goal is incremental impact, as in marketing campaigns, incentives, and notifications, this paradigm systematically misallocates resources toward use…

• Observational Policy Ranking for SMB Financial Guidance from Multi-Action Accounting Logs

  Shrutendra Harsola, Vignesh Subrahmaniam, Vikas Raturi, Kamalika Das, Xiang Gao, Kratika Gupta, Ruocheng Guo, Padmaja Jonnalagedda, Ananya Pramod, Sricharan Kumar · 12 de agosto de 2026

  Small and medium-sized businesses need timely financial guidance, yet historical accounting logs record self-selected and often co-occurring business changes rather than randomized recommendations. We formulate this setting as observational policy ranking: from pre-decision financial information, a …

• Causal State-Space Model for Causal Inference: Estimating Longitudinal Individual Treatment Effects

  Abisoye Abidakun, Mingjun Zhong, Georgios Leontidis · 11 de agosto de 2026

  Estimating counterfactual outcomes over time from longitudinal observational data is central to clinical decision support. Existing methods rely on domain confusion -- adversarial training that renders representations invariant to treatment assignment -- yet this invariance creates a mutual informat…

• CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data

  Gerrit Gro{\ss}mann, Sumantrak Mukherjee, Sebastian J. Vollmer · 11 de agosto de 2026

  Learning fine-grained spatial patterns from coarse-resolution data is challenging, especially in causal settings where high-resolution effects must be inferred from aggregated interventions and outcomes. We introduce CLAM, a method for estimating localized causal effects from coarse observations by …

• A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning

  Jianhan Zhang, Jitao Wang, John D. Piette, Donglin Zeng, Chengchun Shi, Zhenke Wu · 11 de agosto de 2026

  Reinforcement learning (RL) seeks to optimize sequential decisions to maximize population-level benefits over time. However, when deployed in high-stakes settings such as healthcare, RL decisions might systematically restrict some subpopulation's access to valuable services in a manner contrary to t…

• Target-Weighted Neyman Allocation: Experimental Design for Heterogeneous Treatment Effects under Population Shift

  Hoang Dang, Luan Pham, Minh Nguyen · 10 de agosto de 2026

  Randomized experiments are often run in one population to guide decisions in another. Allocating by experimental proportions wastes budget on groups that rarely appear in deployment, whereas allocating by deployment proportions under-samples groups that are hard to measure precisely. We propose \tex…

• A primer on optimal transport for causal inference with observational data

  Florian F Gunsilius · 10 de agosto de 2026

  The theory of optimal transportation has developed into a powerful and elegant framework for comparing probability distributions, with wide-ranging applications in all areas of science. The fundamental idea of analyzing probabilities by comparing their underlying state space naturally aligns with th…